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	Bayesian Fractional Factorial Design	Central Composite Design
Dziedzina	Planowanie eksperymentów	Planowanie eksperymentów
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	1990s	1951
Twórca	DuMouchel & Jones; Chipman, Hamada & Wu	George E. P. Box and K. B. Wilson
Typ	Bayesian experimental design method	Response surface experimental design
Źródło pierwotne	DuMouchel, W., & Jones, B. (1994). A simple Bayesian modification of D-optimal designs to reduce dependence on an assumed model. Technometrics, 36(1), 37–47.	Box, G. E. P., & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society: Series B, 13(1), 1–45.
Inne nazwy	Bayesian FFD, Bayesian screening design, Bayesian factor-screening experiment, BFF design	CCD, Box-Wilson design, central composite response surface design, rotatable central composite design
Pokrewne	3	3
Podsumowanie	Bayesian fractional factorial design integrates Bayesian prior information into the selection and analysis of fractional factorial experiments. Rather than running every combination of factor levels, only a carefully chosen subset of runs is executed, with Bayesian inference used to estimate effects and quantify uncertainty — even when the classical aliasing structure leaves effects confounded.	Central Composite Design (CCD) is a second-order response surface design that allows researchers to efficiently fit a full quadratic model relating multiple continuous input factors to one or more response variables. Introduced by Box and Wilson in 1951, it combines a factorial (or fractional factorial) core, axial (star) points, and center-point replicates into a single unified design, making it the most widely used design for process optimization in engineering, chemistry, and manufacturing.